The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Healthcare fraud accounts for an estimated $60-80 billion dollars/year in waste. Some estimate that the damages constitute 3-10% of all healthcare expenditures. One source of fraud is prescription drug fraud. Examples of prescription fraud include forging prescriptions, altering prescriptions, stealing prescription pads, calling in prescriptions or using online pharmacies, doctor/pharmacy shopping (for example, going to multiple doctors, emergency rooms, or pharmacies and seeking prescriptions while faking symptoms such as migraine headaches, toothaches, cancer, psychiatric disorders, and attention deficit disorder, or having deliberately injured oneself), going across state lines to seek fulfillment at multiple pharmacies, refilling prescriptions before ninety days, and so forth. Prescription fraud primarily occurs at retailer pharmacies, and primarily with narcotics, anti-anxiety medications, muscle relaxants, and hypnotics.
Other sources of fraud include insurance claims fraud such as a provider charging more than peers for services, a provider billing for more tests per patient than peers, a provider billing for unlikely or unnecessary medical procedures, upcoding of services or billing for the most expensive of options, upcoding of equipment or billing for a more expensive item and delivering a lower cost item, consistently billing for high cost medical equipment, such as Durable Medical Equipment, billing for procedures or services not provided, filing duplicate claims that bill for the same service on two separate occasions, unbundling a group of services so that the services billed one at a time yield more compensation than if they had been bundled together, kickbacks from referrals, transportation fraud, collecting money from multiple insurance providers, using surgical modifiers to increase reimbursement, fraud involving viatical health and life insurance, nursing home fraud such as lack of services rendered or services rendered by non-licensed professionals, and so forth.
Prescription claims, doctor office claims, medical procedure claims, hospital claims, medical equipment claims, and other medical claims (collectively referred to as medical claims or healthcare claims) may number in the millions or billions per year. And each medical claim may include numerous types of data, such as billing codes, patient identifier, location, service provider identifier, service date, and the like. Thus, while databases of medical claims contain vast amount of information, selectively mining the available information for useful purposes is not a trivial task.
Techniques for detecting medical claims fraud may include automated and manual processes. For example, although potentially fraudulent medical claims (referred to as fraud leads) can be identified using automated techniques, whether or not to take further action on particular ones of the fraud leads (e.g., investigate, deny reimbursement, notify authorities, pursue remedial action, hold for additional available information, etc.) may require human analysis and decision-making. When provided with a list of fraud leads, however, persons (referred to as fraud analysts) tasked with analyzing or vetting these identified leads may be overwhelmed by the large number of leads in the list. Lists may also lack context and/or useful information for fraud analysts to make an accurate and/or efficient assessment about whether to take further action on particular ones of the identified leads.